With Big Data, Context is a Big Issue

Old-school Big Data: A huge disk from the c1967 Atlas Disc file. Is it time to go new-school and get contextual? Image: dullhunk/Flickr

In the war on noise, contextual applications serve as crucial ammunition.

The Age of Content

With all the hype around Big Data, we’ve become extremely proficient at collecting data – be it from enterprise systems, documents, social interactions, or e-mail and collaboration services. The expanding smorgasbord of data collection points are turning increasingly portable and personal, including mobile phones and wearable sensors, resulting in a data mining gold rush that will soon have companies and organizations accruing Yottabytes (10^24) of data.

To put things into perspective, 1 Exabyte (10^18) of data is created on the internet daily, amounting to roughly the equivalent of data in 250 million DVDs. Humankind produces in two days the same amount of data it took from the dawn of civilization until 2003 to generate, and as the Internet of Things become a reality and more physical objects become connected to the internet, we will enter the Brontobyte (10^27) Era.

So it’s all dandy that we’re getting better and better at sucking up every trace of potential information out there, but what do we do with these mountains of data? Move over Age of Content, enter the Age of Context.

The Age of Context

Big Data has limited value if not paired with its younger and more intelligent sibling, Context. When looking at unstructured data, for instance, we may encounter the number “31” and have no idea what that number means, whether it is the number of days in the month, the amount of dollars a stock increased over the past week, or the number of items sold today. Naked number “31” could mean anything, without the layers of context that explain who stated the data, what type of data is it, when and where it was stated, what else was going on in the world when this data was stated, and so forth. Clearly, data and knowledge are not the same thing.

Take the example of a company that has invested heavily in business intelligence (BI) software that organizes internal data. In an increasingly connected world, this company has not leveraged its data to its potential. Why not? Because the company’s internal data is isolated from the rest of the data universe including news, social media, blogs, and other relevant sources. What if you could join the dots between all these data sources and surface hidden connections?

Using an application that, unlike the average BI product, pulls in information from the entire data universe would help the company answer questions like “Why did our sales plummet last month?” as opposed to just “What happened to our sales figures last month?”

The company could further question, “Did our slip in sales have anything to do with the recent elections and uncertainty?” and “How can we make sure our sales do not slip next time there is a shift in the political landscape?” Identifying potential causality is key in spotting patterns that enable prediction.

For organizations and businesses to survive today, they have to contextualize their data. Just as a doctor diagnosing a patient with diabetes based on body temperature alone is incorrect, so is making business decisions derived from data out of context. A doctor needs to know about the patient’s age, lifestyle, diet, weight, family history, and more in order to make a probable and guarded diagnosis and prognosis. Contextualization is crucial in transforming senseless data into real information – information that can be used as actionable insights that enable intelligent corporate decision-making.

At the end of the day, our overworked minds want to be spoon-fed insights. We want key signals and tightly packaged summaries of relevant, intriguing information that will arm us with knowledge and augment our intelligence.

But how do we extract real intelligence from data?

Extract Real Intelligence From Data

Besides cross-referencing internal data with a plethora of other sources, we need algorithms to boil off the noise and extract the signals, or real human meaning, from the data. What do we mean? Let’s say you have a million tweets from New York City on the eve of the U.S. elections and rather than read them all, you want to know quickly how people are feeling based on these tweets.

To do this you must apply complex algorithms derived from machine learning, computational linguistics, and natural language processing to harvest the key words and corresponding emotions from the tweets. Then you get the key signals: Are people feeling anxious? Hopeful? Confident? Fearful? This is precisely what we do here at Augify: Paving the way for the future of understanding by surfacing signals from the noise in our cloud-based, algorithm-packed product. We wrap the signals in a dashboard of slickly designed, color-coded gauges and visualizations that enhance your understanding of key insights.

Getting Contextual

So it’s great that applications out there can gather and analyze data, detect human-based meaning from it, and visualize it all, but any application is limiting itself if it is only useful once you open the application and enter a query. We wanted to go beyond this. That’s why we decided to get contextual on you and increase our technology’s reach. We go beyond contextual data by developing contextual applications – we wanted to put data to good use by applying it to real-life situations in our daily lives.

The Don of ubiquitous computing, Mark Weiser, stated in 1991 that “the most profound technologies are those that disappear. They weave themselves into the fabric of our everyday life until they are indistinguishable from it,” much like the telephone or electricity. They have seeped into our surroundings, playing an integral role in our everyday lives.

Modern day examples include adaptive technologies such as Google Now, which tracks your online behavior and uses this data to predict the information that you will need, such as local traffic or weather updates. Similarly, “learning thermostat” Nest self-adjusts your home’s temperature based on your activity, saving energy usage and bills. Pervasive technologies mean they are everywhere and nowhere at the same time – an invisible layer listening to your actions in order to be more helpful.

Along the same vein, we believe in pervasive Augmented Intelligence, meaning that you can reap the benefits of Augify’s signal-detecting technology anywhere you go in the connected world. So even when you are working outside of the Augify application screen, be it writing an e-mail or reading a website, Augify’s algorithms will work silently and invisibly in the background, culling key insights from the text you are reading at that very moment and feeding them to you in a pop-up window.

Pervasive Augmented Intelligence

Imagine being able to save time reading long text by getting a synopsis of the key themes, topics, emotions, people, and entities in a news article? By seeing the emotions, sentiments, intentions, and credibility in someone’s Twitter feed, and by spotting the key influencers in their network? What if, as you typed an e-mail to your boss about a new enterprise software, you received pop-up recommendations of credible reference articles related to said software?

In other words, what if you could detect the important signals in any web content in real-time? With Augify’s contextual application, this is truly possible, enabling you to augment your intelligence anywhere your online activity takes you.

Augment Your Intelligence Anywhere Your Online Activity Takes You

The Age of Context demands that contextual data be applied to everyday situations in useful ways. How do we make use of this data? Since we’ve gotten good at collecting data, now it’s all about putting it into context and making sense out of it – mining for the nuggets of insights that answer the “So What?” question. Data is meaningless and even cumbersome without context – the key holistic and interpretive lens through which data is filtered and turned into real information.